Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images

Ying Zhang, Shijie Chen, Yuling Wang, Jingjing Li, Kai Xu, Jyhcheng Chen, Jie Zhao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Microsatellite instability (MSI) is one of the essential tumor biomarkers for cancer treatment and prognosis. The presence of more significant PD-L1 expression on the surface of tumor cells in endometrial cancer with MSI suggests that MSI may be a promising biomarker for anti-PD-1/PD-L1 immunotherapy. However, the conventional testing methods are labor-intensive and expensive for patients. Methods: Inspired by classifiers for MSI based on fast and low-cost deep-learning methods in previous investigations, a new architecture for MSI classification based on an attention module is proposed to extract features from pathological images. Especially, slide-level microsatellite status will be obtained by the bag of words method to aggregate probabilities predicted by the proposed model. The H&E-stained whole slide images (WSIs) from The Cancer Genome Atlas endometrial cohort are collected as the dataset. The performances of the proposed model were primarily evaluated by the area under the receiver-operating characteristic curve, accuracy, sensitivity, and F1-Score. Results: On the randomly divided test dataset, the proposed model achieved an accuracy of 0.80, a sensitivity of 0.857, a F1-Score of 0.826, and an AUROC of 0.799. We then visualize the results of the microsatellite status classification to capture more specific morphological features, helping pathologists better understand how deep learning performs the classification. Conclusions: This study implements the prediction of microsatellite status in endometrial cancer cases using deep-learning methods directly from H&E-stained WSIs. The proposed architecture can help the model capture more valuable features for classification. In contrast to current laboratory testing methods, the proposed model creates a more convenient screening tool for rapid automated testing for patients. This method can potentially be a clinical method for detecting the microsatellite status of endometrial cancer.

Original languageEnglish
JournalJournal of Cancer Research and Clinical Oncology
DOIs
StateAccepted/In press - 2023

Keywords

  • Attention
  • Deep learning
  • Endometrial cancer
  • Microsatellite instability
  • Whole slide image

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